多层感知机的从零开始.ipynb

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  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "83356650",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "from d2l import torch as d2l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "356d653e",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size=256\n",
    "train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)\n",
    "\n",
    "## 定义输入形状 -> 设置初始参数 -> 激活函数 -> 模型 -> 损失函数 -> 定义超参数 -> 选择优化器 -> 启动训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0830e8ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_inputs, num_outputs, num_hiddens = 784, 10, 256  # 输入和输出是数据决定的，num_hiddens是隐藏层\n",
    "\n",
    "# 在 PyTorch 中，模型的权重和偏置通常被封装为 nn.Parameter 对象，这样它们就会被自动追踪为需要梯度的参数，并在反向传播时计算梯度。\n",
    "# nn.Parameter()只是为了告知是参数，需要自动求导\n",
    "\n",
    "# 梯度值是顿时函数相对于模型参数的导数\n",
    "# 梯度爆炸：由于梯度值过大，导致权重更新过大，从而使模型参数迅速变得非常大，进而导致模型输出极端值\n",
    "# 避免梯度爆炸：将初始权重设置为极小值\n",
    "\n",
    "\n",
    "W1 = nn.Parameter(torch.randn(num_inputs, num_hiddens, requires_grad=True) * 0.01)  # torch.randn()从(0,1)的正态分布中随机采样。\n",
    "b1 = nn.Parameter(torch.zeros(num_hiddens, requires_grad=True))\n",
    "W2 = nn.Parameter(torch.randn(num_hiddens, num_outputs, requires_grad=True) * 0.01)\n",
    "b2 = nn.Parameter(torch.zeros(num_outputs, requires_grad=True))\n",
    "\n",
    "params = [W1, b1, W2, b2]  # W1,b1是第一层；W2,b2是第二层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "aea68dd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "## 激活函数ReLU：当输入大于0时，输出等于输入；当输入小于或等于0时，输出为0\n",
    "# 如果没有激活函数，无论多少层，输出总是输入的线性组合。\n",
    "# ReLU效果：如果输入信号足够强（即加权求和的结果为正），神经元就“激活”，输出这个信号；如果输入信号不够强，神经元就“不激活”，输出为0。\n",
    "\n",
    "def relu(X):\n",
    "    a = torch.zeros_like(X)\n",
    "    return torch.max(X, a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e23d053b",
   "metadata": {},
   "outputs": [],
   "source": [
    "## 在线性组合上，应用激活函数作为模型。\n",
    "def net(X):\n",
    "    X = X.reshape((-1, num_inputs))  # num_inputs 是网络输入层的神经元数量，是隐藏的展平层\n",
    "    H = relu(X@W1 + b1)  # 这里“@”代表矩阵乘法\n",
    "    return (H@W2 + b2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0cb09449",
   "metadata": {},
   "outputs": [],
   "source": [
    "loss = nn.CrossEntropyLoss(reduction='none')  # 计算交叉熵损失（reduction='none' 输出每个样本的损失值）\n",
    "\n",
    "# 把网络输出的未归一化的预测传到Loss，在里面过Softmax做Cross-Entropy\n",
    "# 结合了 nn.LogSoftmax 和 nn.NLLLoss（负对数似然损失）两个操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3c579691",
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   "source": [
    "num_epochs, lr = 10, 0.1\n",
    "updater = torch.optim.SGD(params, lr=lr)\n",
    "d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, updater)  \n",
    "# 接受模型、训练数据迭代器、测试数据迭代器、损失函数、训练周期数和优化器作为参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4423d4c9",
   "metadata": {},
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      "text/plain": [
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d2l.predict_ch3(net, test_iter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce0c6d0b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ebc9f13",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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